International Journal of Aquatic Biology (2014) 2(6): 330-336
ISSN: 2322-5270; P-ISSN: 2383-0956
Journal homepage: www.NPAJournals.com
© 2013 NPAJournals. All rights reserved
Original Article
Morphological variation of shad fish Alosa brashnicowi (Teleostei, Clupeidae) populations
along the southern Caspian Sea coasts, using a truss system
Sara Paknejad1, Adeleh Heidari2, Hamed Mousavi-Sabet*21
1Department of Fisheries, Islamic Azad University, Tonekabon Branch, Mazandaran, Iran.
2Department of Fisheries, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, P.O. Box: 1144, Guilan, Iran.
Article history:
Received 1 July 2014
Accepted 5 October 2014
Available online 2 5 December 2014
Keywords:
Caspian Sea
Alosa braschnicowi
Truss system
Shad fish
Morphometrics
Abstract: A 15-landmark morphometric truss network system was used to investigate the hypothesis
of population fragmentation of Shad fish Alosa braschnicowi Borodin, 1904 along the southern
Caspian Sea. A total of 181 A. braschnicowi specimens were caught from six localities, respectively
from the west to the east including; Astara, Rezvanshahr, Anzali, Tonekabon, Sari and Miankale.
Principal component analysis, canonical variates analysis and clustering analysis were used to
examine morphological differences. Univariate analysis of variance showed significant differences
between the means of the six groups for 72 standardized morphometric measurements out of 105
characters studied. In canonical variates analysis, the overall assignment of individuals into their
original groups was 71.46% and scatter plot of individual component scores between CV1 and CV2
showed fish specimens grouped into six areas. Clustering analysis based on Euclidean square distances
among groups of centroids using UPGMA resulted into six main clusters indicating morphologically
distinction populations of A. braschnicowi in the region. These populations of A. braschnicowi are
distinguished especially by head shape, eye diameter, and pre-dorsal, pre-pelvic and pre-anal
distances. Therefore, it is suggested considering these morphologically different populations as
distinct stock in the southern Caspian Sea coasts.
Introduction
The study of morphological characters with the aim
of defining or characterizing fish stock units, has a
great interest in ichthyology (Bektas and Belduz,
2009). The morphometric characters is particularly
important where the differences are mostly attributed
to environmental influences rather than genetic
differentiation (Bektas and Belduz, 2009).
Geographical isolation of populations and
interbreeding can lead to morphometric variations
between populations, and this morphometric
variation can provide a basis for population
differentiation (Bookstein, 1991).
The family Clupeidae is found in warmer marine
waters with some anadromous or permanent
freshwater residents. This family has about 200
* Corresponding author: Hamed Mousavi-Sabet
E-mail address: mousavi-sabet@guilan.ac.ir
species in 56 genera worldwide (Eschmeyer and
Fong, 2011; Coad, 2014), with eight reported species
in the Caspian Sea. Alosa braschnicowi is an
economically important clupeids of the Caspian Sea
that widely distributed across this sea. This species
distributes in the south in winter, moving north to
spawn in spring (Coad, 2014). The morphometric
characters between male and female sexes in this
species did not different (Whitehead, 1985). The
study of morphometrics using the truss network
system is effective in capturing information about
the shape of an organism (Kocovsky et al., 2009). It
covers the entire fish in a uniform network, and
theoretically, increases the likelihood of extracting
morphometric differences between specimens
(Kocovsky et al., 2009; Cakmak and Alp, 2010).
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Paknejad et al/ Morphological variation of A. brashnicowi along the southern Caspian Sea coasts
Therefore, it has been used in the differentiation of
various populations within a species and also various
species (Kocovsky et al., 2009). Various effects of
the geographical isolation on fish population in the
southern Caspian Sea basin have been documented
in the recent past (Mousavi-Sabet et al., 2011;
Mousavi-Sabet et al., 2012; Mousavi-Sabet and
Anvarifar, 2013; Kohestan-Eskandari et al., 2014).
However, the variability of the A. braschnicowi
population parameters and its spatial distribution has
not been studied in Iranian waters of the Caspian
Sea. Therefore, propose of this study was to use a set
of morphometric characters for examine whether
specific ecological constraints, due to geographic
variation, could affect the formation of stock
separation for this species.
Materials and methods
A total of 181 specimens of A. braschnicowi were
randomly collected by beach seine from six fishing
regions along the southern Caspian Sea coasts,
including the Miankale (36°54′10.89″ N,
53°48′48.33″ E; 31 individuals), Sari (36°48′04.63″
N, 53°02′07.50″ E; 30 individuals), Tonekabon
(36°50′20.97″N, 50°50′17.25″E; 30 individuals),
Anzali (37°29′29.86″ N, 49°27′39.59″ E; 30
individuals), Rezvanshahr (37°36′32.19″ N,
49°08′25.54″ E; 30 individuals) and Astara
(38°23′47.40″ N, 48°54′10.17″ E; 30 individuals) in
November 2011 (Fig. 1). The sampled fish were
fixed in 10% formaldehyde at the sampling sites and
transported to the laboratory.
For extracting morphometric data, the left side of
fishes were photographed by a 300-dpi, 32-bit color
digital camera (Cybershot DSC-F505; Sony, Japan)
with dorsal and anal fins erected by pins. A total of
105 distance measurements between 15 landmarks
were surveyed using the truss network system
according to Bookstein (1991) with minor
modifications (Fig. 2). Images were saved in jpg
format, and the defined landmark points were
digitized using TpsDig2 (Mustafic et al., 2008) on
pictures. A box truss of 26 lines connecting the
landmark points was generated for each fish to
represent the basic shape of fish (Bookstein, 1991;
Mustafic et al., 2008). All measurements
transformed into linear distances for subsequent
analysis (Mustafic et al., 2008). An allometric
method was used to remove size-dependent variation
in morphometric characters using following
formula: Madj = M (Ls / L0)b, where M is the original
measurement, Madj the size adjusted measurement,
L0 the standard length of the fish, Ls the overall mean
of the standard length for all fish from all samples in
each analysis, and b was estimated for each character
from the observed data as the slope of the regression
of log M on log L0 using all fish in any group. The
results derived from the allometric method were
confirmed by testing significance of the correlation
between transformed variables and standard length
(Mustafic et al., 2008).
The sex of specimens was determined
macroscopically, and there were no significant
differences in tested variables between the sexes
within the same stock. Therefore, the data for both
Figure 1. Sampling site locations of A. braschnicowi along the
southern Caspian Sea coast.
Figure 2. Locations of the 15 landmarks for constructing the truss
network on A. braschnicowi. 1- tip of snout; 2- anterior edge of
eye; 3- posterior edge of eye; 4- posterior tip of maxillary; 5-
forehead (end of frontal bone); 6- end of operculum; 7- dorsal
origin of pectoral fin; 8- origin of dorsal fin; 9- origin of pelvic
fin; 10- termination of dorsal fin; 11- origin of anal fin; 12-
termination of anal fin; 13- dorsal side of caudal peduncle, at the
nadir; 14- ventral side of caudal peduncle, at the nadir; 15- end of
body lateral line.
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International Journal of Aquatic Biology (2014) 2(6): 330-336
sexes were pooled for all subsequent analyses.
Univariate analysis of variance (ANOVA) was
performed for each morphometric character to
evaluate the significant difference between the six
populations (Rodriguez et al., 2010). Those
morphometric characters which showed highly
significant variations (P≤0.01) were used to achieve
the recommended ratio of the number of organisms
measured (N) to the parameters included (P) in the
analysis of at least 3–3.5 (Bookstein, 1991) to obtain
a stable outcome from multivariate analysis. The
principal component analysis (PCA), canonical
variates analysis (CVA) and cluster analysis (CA) by
adopting the Euclidean square distance as a measure
of dissimilarity and UPGMA (Unweighted Pair
Group Method with Arithmetical average) as the
clustering algorithm (Yakubu et al., 2011) were
employed to discriminate the six populations.
Statistical analyses for morphometric data were
performed using the SPSS version 16 software
package.
To determine the most effective morphometric
Characters F P Characters F P Characters F P Characters F P
1-2 0.000 1.000 3-4 2.801 0.019 5-9 7.800 0.000 8-11 3.636 0.004
1-3 25.690 0.000 3-5 6.842 0.000 5-10 3.547 0.005 8-12 6.034 0.000
1-4 11.691 0.000 3-6 2.720 0.022 5-11 4.875 0.000 8-13 1.980 0.085
1-5 6.637 0.000 3-7 1.169 0.327 5-12 3.665 0.004 8-14 5.013 0.000
1-6 13.629 0.000 3-8 7.566 0.000 5-13 5.467 0.000 8-15 4.246 0.001
1-7 12.917 0.000 3-9 3.790 0.003 5-14 2.905 0.015 9-10 0.517 0.763
1-8 7.118 0.000 3-10 5.837 0.000 5-15 3.658 0.004 9-11 4.800 0.000
1-9 3.654 0.004 3-11 3.430 0.006 6-7 9.633 0.000 9-12 1.729 0.131
1-10 1.298 0.267 3-12 5.928 0.000 6-8 7.476 0.000 9-13 1.807 0.114
1-11 5.886 0.000 3-13 3.609 0.004 6-9 3.316 0.007 9-14 2.028 0.078
1-12 3.354 0.007 3-14 4.244 0.001 6-10 8.110 0.000 9-15 1.606 0.161
1-13 5.519 0.000 3-15 7.471 0.000 6-11 3.348 0.007 10-11 1.960 0.088
1-14 6.359 0.000 4-5 9.823 0.000 6-12 6.974 0.000 10-12 1.401 0.227
2-3 28.648 0.000 4-6 3.275 0.008 6-13 5.541 0.000 10-13 0.443 0.818
2-4 9.007 0.000 4-7 0.086 0.994 6-14 4.937 0.000 10-14 0.822 0.536
2-5 7.263 0.000 4-8 3.720 0.003 6-15 9.226 0.000 10-15 0.425 0.831
2-6 14.608 0.000 4-9 2.135 0.064 7-8 2.386 0.041 11-12 2.907 0.015
2-7 14.325 0.000 4-10 2.523 0.032 7-9 5.660 0.000 11-13 3.253 0.008
2-8 7.073 0.000 4-11 1.837 0.109 7-10 3.010 0.012 11-14 1.164 0.330
2-9 2.917 0.000 4-12 2.746 0.021 7-11 2.020 0.079 11-15 1.951 0.089
2-10 1.472 0.202 4-13 1.659 0.148 7-12 4.070 0.002 12-13 1.746 0.127
2-11 5.020 0.000 4-14 1.945 0.090 7-13 2.921 0.015 12-14 0.410 0.842
2-12 1.167 0.328 4-15 3.272 0.008 7-14 2.026 0.078 12-15 2.963 0.014
2-13 2.895 0.016 5-6 12.284 0.000 7-15 4.412 0.001 13-14 0.837 0.525
2-14 4.422 0.001 5-7 18.966 0.000 8-9 1.209 0.307 13-15 0.389 0.856
2-15 2.595 0.028 5-8 8.181 0.000 8-10 3.924 0.002 14-15 0.998 0.421
Table 1. The results of ANOVA for morphometric measurements of A. braschnicowi populations along the southern Caspian Sea.
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Paknejad et al/ Morphological variation of A. brashnicowi along the southern Caspian Sea coasts
measurement to differentiate studied populations,
the contributions of variables to principal
components (PC) were examined. To examine the
suitability of the data for PCA, Bartlett’s Test of
sphericity and the Kaiser–Meyer–Olkin (KMO)
measures were performed. The Bartlett’s Test of
sphericity, tests the hypothesis that the values of the
correlation matrix equal zero and the KMO measure
of sampling adequacy tests, whether the partial
correlation among variables is sufficiently high
(Yakubu et al., 2011). The KMO statistics vary
between 0 and 1 and the values greater than 0.5 are
acceptable (Nimalathasan, 2009; Yakubu et al.,
2011).
Results
The correlation between transformed morphometric
variables and standard length was not significant
(P>0.05) which confirms that size or allometric
signature on the basic morphological data was
accounted. Significant differences between six
populations of A. braschnicowi were observed in
terms of 72 morphometric characters out of 105
studied (Table 1). Of these 72 characters, 60
characters were found to be highly significant
(P≤0.01) and were used for further multivariate
analysis. In this study N:P ratio was 3.01 (181/60)
that revealed samples size were adequate.
The value of KMO for overall matrix is 0.695, and
the Bartlett’s Test of sphericity is significant
(P≤0.01). The results of KMO and Bartlett’s suggest
that the sampled data is appropriate to proceed with
a factor analysis procedure.
Principal component analysis of 60 morphometric
measurements extracted 12 factors with Eigen
values>1, explaining 94.52% of the variance (Table
2). The first principal component (PC1) accounted
for 25.94% of the variation and the second principal
component (PC2) for 15.33% (Table 2), and the most
significant loadings on PC1 were 1-3, 1-4, 1-6, 1-7,
1-9, 1-11, 1-12, 1-13, 1-14, 2-3, 2-6, 2-7, 2-11, 3-13,
3-14, 3-15, 4-6, 5-6, 5-7, 5-9, 6-8, 6-10, 6-11, 6-12,
6-13 6-14, 6-15, 7-15 and on PC2 were 1-8, 2-8, 3-
8, 4-8, 5-8, 6-8, 8-10, 8-11, 8-12, 8-14, 8-15. In this
analysis, the characteristics with an Eigen value
exceeding 1 were included, and others discarded.
The CV1 and CV2 were plotted to allow visual
examination of the distribution of each locality
sample along the CVs (Fig. 3). In the scatter plot, the
Miankale, Astara and Anzali specimens were
grouped together in the same quadrant with high
value for CV1 and low value for CV2 (quadrate IV),
Tonekabon in a quadrant with low value for both
CVs (quadrate III), Rezvanshahr in a quadrant with
Factor Eigenvalues
Percentage of
variance
Percentage of
cumulative variance
1 15.567 25.945 25.945
2 9.203 15.338 41.283
3 7.332 12.220 53.503
4 6.235 10.392 63.895
5 3.961 6.601 70.496
6 3.198 5.330 75.826
7 2.662 4.437 80.263
8 2.609 4.348 84.611
9 1.907 3.179 87.789
10 1.771 2.952 90.741
11 1.178 1.963 92.704
12 1.094 1.823 94.527
Table 2. Eigen values, percentage of variance and percentage of cumulative variance of principal component analysis of morphometric
measurements for A. braschnicowi populations along the southern Caspian Sea.
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International Journal of Aquatic Biology (2014) 2(6): 330-336
high value for both CVs (quadrate II) and finally Sari
in a quadrant with low value for CV1 and high value
for CV2 (quadrate I). The overall random
assignment of individuals into their original groups
was high (71.46%), indicating that these samples are
probably divergent from each other (Fig. 3).
The dendrogram derived from CA of Euclidean
square distances among groups of centroids showed
six main clusters based on morphometric
characteristics. Despite the geographical distance
between the Miankale and Sari regions and
Rezvanshahr and Anzali regions, morphometric
clustering revealed that the individuals of these
locations form the same clade with great
homogeneity, while Tonekabon and Astara exhibited
higher heterogeneity, confirming the results obtained
from discriminant analysis for this species (Fig. 4).
Discussion
The multivariate analysis of morphometric
characteristics classified the A. braschnicowi
populations along the southern Caspian Sea coasts
into six distinct groups. The results demonstrate that
there are morphologically distinct populations of
Figure 3. Scatterplot of group centroids of standardized canonical variates functions 1 (CV1) and 2 (CV2) for morphometric characteristics of six
populations of A. braschnicowi along the southern Caspian Sea.
Figure 4. Dendrogram derived from cluster analyses of morphometric measurements on the basis of Euclidean distance for A. braschnicowi
populations along the southern Caspian Sea.
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Paknejad et al/ Morphological variation of A. brashnicowi along the southern Caspian Sea coasts
A. braschnicowi particularly for Tonekabon region.
These morphological differences is solely related to
body shape variation and not to size effects which
were successfully accounted by allometric
transformation. Size-related traits play a
predominant role in morphometric analysis and the
results may be erroneous if not removed from data
(Bookstein, 1991; Buj et al., 2008; Torres et al.,
2010).
The analysis of variance also revealed significant
phenotypic variation among the six populations.
CVA could be a useful method to distinguish
different stocks of the same species (Yakubu and
Okunsebor, 2011). The present study showed a high
differentiation among the populations of
A. braschnicowi in the studied areas. This
segregation was partly confirmed by PCA, where
revealed that the populations were distinct from each
other.
The causes of morphological differences among
populations are often quite difficult to explain
(Bookstein, 1991). It has been suggested that the
morphological characteristics of fishes are
determined by genetic, environment and the
interaction between them (Heidari et al., 2013;
Kohestan-Eskandari et al., 2014). The environmental
factors prevailing during the early development
stages, when the individual’s phenotype is more
amenable to environmental influence is of particular
importance (Eschmeyer and Fong, 2011). The
phenotypic variability may not necessarily reflect
population differentiation at the molecular level
(Bookstein, 1991). Apparently, different
environmental conditions can lead to an
enhancement of pre-existing genetic differences,
providing a high interpopulation structuring
(Eschmeyer and Fong, 2011; Heidari et al., 2013;
Mousavi-Sabet and Anvarifar, 2013).
The Tonekabon (in the south-central part of the
Caspian Sea), Miankale (in the southeast part of the
sea) and Astara (in the southwest part of the sea)
specimens are more distinct from the others. The
distinctive environmental conditions of the
Tonekabon, Miankale and Astara relative to the
other studied areas may underlie the morphological
differentiation among these three populations. The
studied populations are distinguished from each
other by morphologic differences especially in head
shape, eye diameter, and pre-dorsal, pre-pelvic and
pre-anal distances.
Geographical isolation can also affect growth pattern
and reproductive strategy of fish species. The
importance of such factors on producing
morphological differentiation in fish species is well-
known (Yamamoto et al., 2006; Pollar et al., 2007;
Heidari et al., 2013).
As conclusion, the present study proposes high
morphological differentiation among
A. braschnicowi populations along the southern
Caspian Sea coasts. The results also suggest these
morphologically different populations should be
considered as distinct stock in the southern Caspian
Sea in fisheries management and commercial
exploitation of this species and any stock
enhancement program. Nevertheless, future studies
on determination of population structure will be
elucidated using biochemical and molecular genetics
methods.
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